AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital.
European journal of hospital pharmacy : science and practice
Jun 24, 2025
OBJECTIVES: Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, thei...
Pharmacovigilance in the digital age: gaining insight from social media data.
Experimental biology and medicine (Maywood, N.J.)
May 27, 2025
Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous reporting systems and clinical trials are valuable for identifying adverse drug events, but face de...
Artificial Intelligence: Applications in Pharmacovigilance Signal Management.
Pharmaceutical medicine
Apr 21, 2025
Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing monitoring and understanding of those products' safety profiles. Part of this process, signal manageme...
An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).
Scientific reports
Apr 16, 2025
The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of el...
Deep Learning
Constipation
Digestive System Diseases
Humans
Big Data
Feasibility Studies
Nausea
Predictive Learning Models
Reproducibility of Results
Diarrhea
Drug-Related Side Effects and Adverse Reactions
Vomiting
ROC Curve
Databases, Pharmaceutical
Gastrointestinal Tract
Area Under Curve
Biological Clocks
Electrodiagnosis
TransformDDI: The Transformer-Based Joint Multi-Task Model for End-to-End Drug-Drug Interaction Extraction.
IEEE journal of biomedical and health informatics
Apr 4, 2025
Drug-Drug Interactions (DDI) identification is a part of the drug safety process, that focuses at avoiding potential adverse drug effects that can lead to patient health risks. With the exponential growth in published literature, it becomes increasin...
Narrative Search Engine for Case Series Assessment Supported by Artificial Intelligence Query Suggestions.
Drug safety
Mar 15, 2025
INTRODUCTION: Manual identification of case narratives with specific relevant information can be challenging when working with large numbers of adverse event reports (case series). The process can be supported with a search engine, but building searc...
Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Knowledge of unintended effects of drugs is critical in assessing the risk of treatment and in drug repurposing. Although numerous existing studies predict drug-side effect presence, only four of them predict the frequency of the side effects. Unfort...
A small-scale data driven and graph neural network based toxicity prediction method of compounds.
Computational biology and chemistry
Feb 25, 2025
Toxicity prediction is crucial in drug discovery, helping identify safe compounds and reduce development risks. However, the lack of known toxicity data for most compounds is a major challenge. Recently, data-driven models have gained attention as a ...
Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.
Drug safety
Feb 21, 2025
INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancements in various text processing tasks. However, the applicability of these LLMs in identifying relat...
Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients.
Research in social & administrative pharmacy : RSAP
Feb 12, 2025
INTRODUCTION: Adverse drug reactions (ADRs) significantly impact healthcare systems, leading to increased hospitalization rates and costs. With the growing adoption of artificial intelligence (AI) in healthcare, machine learning (ML) models offer pro...
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